Neural network based adaptive dynamic surface control of nonaffine nonlinear systems with time delay and input hysteresis nonlinearities

Observer-Based Fixed-Time Neural Control for a Class of Nonlinear Systems

This article is concerned with an issue of fixed time adaptive neural control for a class of uncertain nonlinear systems subject to hysteresis input and immeasurable states. The state observer and neural networks (NNs) are used to estimate the immeasurable states and approximate the unknown nonlinearities, respectively. On this foundation, an adaptive fixed time neural control strategy is developed. Technically, this control strategy is based on a novel fixed-time stability criterion. Different from the research on fixed-time control in the conventional literature, this article designs a new controller with two fractional exponential powers. In the light of the established stability criterion, the fixed-time stability of the systems is guaranteed under the proposed control scheme. Finally, a simulation study is carried out to test the performance of the developed control strategy.

An active disturbance rejection control for hysteresis compensation based on Neural Networks adaptive control

In the present paper, an active disturbance rejection control(ADRC) scheme via radial basis function(RBF) neural networks is designed for adaptive control of non-affine nonlinear systems facing hysteresis disturbance in which RBF neural network approximation is utilized to tackle the system uncertainties and ADRC is designed to real-time estimate and compensate disturbance with unknown backlash-like hysteresis. Combining the adaptive neural networks design with ADRC design techniques, a new dual-channel composite controller scheme is developed herein whereby adaptive neural networks are used as feed-forward inverse control and ADRC as closed-loop feedback control. Furthermore, as compared to adaptive neural networks control algorithm, the proposed RBF-ADRC dual-channel composite controller can guarantee that the desired signal can be tracked with a small domain of the origin and it is confirmed to be effective under Lyapunov stability theory and MATLAB simulations.

Neural-Network-Based Adaptive Resilient Dynamic Surface Control Against Unknown Deception Attacks of Uncertain Nonlinear Time-Delay Cyberphysical Systems

A neural-network-based dynamic surface design strategy against sensor and actuator deception attacks is presented to design a delay-independent adaptive resilient control scheme of uncertain nonlinear time-delay cyberphysical systems in the lower triangular form. It is assumed that all nonlinearities, time-varying delays, and sensor and actuator attacks are unknown. In the concerned problem, since the state information measured by sensors is compromised by additional attack signals, the exact state variables are not available for feedback. Thus, a memoryless adaptive resilient control design using compromised state variables is developed by employing the neural-network-based function approximation technique and designing the attack compensator. The resulting control scheme ensures the robust stabilization in the presence of unknown deception attacks and time-varying delays. It is shown from the Lyapunov stability analysis that all closed-loop signals are uniformly ultimately bounded and the stabilization errors converge to an adjustable neighborhood of the origin.